Cooperative Coevolution and Univariate Estimation of Distribution Algorithms

被引：0

作者：

Vo, Christopher ^{[1
]}

Panait, Liviu

Luke, Sean ^{[1
]}

机构：

[1] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA

来源：

FOGA'09: PROCEEDINGS OF THE 10TH ACM SIGRVO CONFERENCE ON FOUNDATIONS OF GENETIC ALGORITHMS | 2009年

关键词：

Cooperative Coevolution; Estimation of Distribution Algorithms; Evolutionary Game Theory;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we discuss a curious relationship between Cooperative Coevolutionary Algorithms (CCEAs) and univariate Estimation of Distribution Algorithms (EDAs). Specifically, the distribution model for univariate EDAs is equivalent to the infinite population EGT model common in the analysis of CCEAs. This relationship may permit cross-pollination between these two disparate fields. As an example, we derive a new EDA based on a known CCEA from the literature, and provide some preliminary experimental analysis of the algorithm.

引用

页码：141 / 150

页数：10

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